Chapter 7 Occupancy

Name a better duo than that camera trapping and occupancy modelling. I'll wait.

Occupancy modelling has been the mainstay of analysing data from camera traps for many years, so learning how to wangle our data into occupancy style format is essential.

7.1 Single species occupancy model

In this example we will use the weekly observations dataframe fro the SingleSiteExploration script.

weekObs <- read.csv("data/processed_data/Algar_30min_Independent_Weekly_observations.csv", header=T)

Which, as a quick reminder, looks like this:

kbl(weekObs)%>%
  kable_paper() %>%
  scroll_box( height = "200px")
Deployment.Location.ID Date Effort Alces.alces Canis.latrans Canis.lupus Cervus.canadensis Grus.canadensis Lepus.americanus Lynx.canadensis Martes.americana Odocoileus.virginianus Rangifer.tarandus Tamiasciurus.hudsonicus Ursus.americanus
Algar08 2018-W45 1 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2018-W50 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar08 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W15 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W16 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W18 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar08 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W20 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar08 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W25 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W26 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W30 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W32 7 2 0 0 0 0 1 0 0 0 0 0 0
Algar08 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W36 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar08 2019-W46 5 0 0 1 0 0 0 0 0 0 0 0 0
Algar27 2018-W45 3 0 0 0 0 0 0 0 0 1 0 0 0
Algar27 2018-W46 7 0 0 0 1 0 0 0 0 0 0 0 0
Algar27 2018-W47 7 0 1 0 0 0 0 0 0 0 0 0 0
Algar27 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2018-W49 7 0 0 2 0 0 0 0 0 0 0 0 0
Algar27 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W00 5 0 1 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W01 7 0 1 1 0 0 0 0 0 0 0 0 0
Algar27 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W04 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W06 7 0 0 1 0 0 0 0 0 0 0 0 0
Algar27 2019-W07 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar27 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 2019-W11 7 0 0 0 0 0 0 2 0 0 0 0 0
Algar27 2019-W12 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar27 2019-W13 4 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2018-W45 3 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2018-W46 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar29 2018-W47 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar29 2018-W48 7 0 0 0 0 0 0 1 0 1 0 0 0
Algar29 2018-W49 7 0 0 1 0 0 0 0 0 0 0 0 0
Algar29 2018-W50 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar29 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W06 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W14 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar29 2019-W15 7 1 0 0 0 0 0 0 0 0 0 0 1
Algar29 2019-W16 7 1 0 0 0 0 0 0 0 1 0 0 0
Algar29 2019-W17 7 0 0 0 0 3 0 0 0 0 0 0 0
Algar29 2019-W18 7 0 0 0 0 2 0 0 0 1 0 0 0
Algar29 2019-W19 7 1 0 0 0 0 0 0 0 1 0 0 0
Algar29 2019-W20 7 0 0 0 0 2 0 0 0 4 0 0 0
Algar29 2019-W21 7 1 0 0 0 2 0 0 0 0 0 0 0
Algar29 2019-W22 7 1 0 0 0 0 0 0 0 0 0 0 2
Algar29 2019-W23 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W25 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar29 2019-W26 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar29 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W30 7 0 0 0 0 2 0 0 0 0 0 0 0
Algar29 2019-W31 7 0 0 0 0 1 0 0 0 0 0 0 1
Algar29 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W33 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar29 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W36 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar29 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 2
Algar29 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar29 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar29 2019-W42 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar29 2019-W43 7 0 0 0 0 0 0 0 0 3 0 0 0
Algar29 2019-W44 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar29 2019-W45 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar29 2019-W46 4 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W45 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W51 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar31 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W11 7 0 0 0 0 0 0 0 2 0 0 0 0
Algar31 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W13 7 1 0 2 0 0 0 0 0 0 0 0 0
Algar31 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W15 7 0 0 1 0 0 0 0 0 0 0 0 0
Algar31 2019-W16 7 0 0 1 0 1 0 0 0 0 0 0 0
Algar31 2019-W17 7 0 0 1 0 0 0 0 0 0 0 0 0
Algar31 2019-W18 7 0 0 0 0 3 0 0 0 0 0 0 0
Algar31 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W21 7 0 0 0 0 0 0 0 1 0 0 0 0
Algar31 2019-W22 7 0 0 0 0 2 0 0 0 0 0 0 0
Algar31 2019-W23 7 1 0 0 0 1 0 0 0 1 1 0 0
Algar31 2019-W24 7 0 0 1 0 1 0 0 0 0 1 0 0
Algar31 2019-W25 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar31 2019-W26 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar31 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W28 7 0 0 0 0 1 0 0 0 0 1 0 0
Algar31 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar31 2019-W30 7 0 0 0 0 2 0 0 0 0 1 0 0
Algar31 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W32 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar31 2019-W33 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar31 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W36 7 0 0 0 0 0 0 0 0 0 2 0 0
Algar31 2019-W37 7 0 0 0 0 0 0 0 0 0 3 0 0
Algar31 2019-W38 7 0 0 0 0 0 0 0 0 0 3 0 0
Algar31 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W41 7 0 0 0 0 0 0 0 0 0 1 0 1
Algar31 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar31 2019-W46 5 0 0 0 0 0 1 0 1 0 0 0 0
Algar32 2018-W45 4 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2018-W46 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar32 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W17 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar32 2019-W18 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W23 7 0 0 0 0 2 0 0 0 0 0 0 0
Algar32 2019-W24 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar32 2019-W25 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W26 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W29 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar32 2019-W30 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W36 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar32 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W43 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar32 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 2019-W46 3 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2018-W45 1 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2018-W47 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar34 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W01 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar34 2019-W02 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar34 2019-W03 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar34 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W06 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar34 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W09 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W12 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar34 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W18 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W25 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W26 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W30 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W36 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 2019-W46 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2018-W45 3 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2018-W49 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar35 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2018-W52 2 0 0 0 0 0 1 0 0 0 0 0 0
Algar35 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W10 7 0 0 0 0 0 4 0 0 0 0 0 0
Algar35 2019-W11 7 0 0 0 0 0 1 0 0 0 0 0 0
Algar35 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W15 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W17 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar35 2019-W18 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar35 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W21 7 1 0 0 0 1 0 0 0 0 0 0 0
Algar35 2019-W22 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar35 2019-W23 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W25 7 2 0 0 0 1 0 0 0 0 0 0 0
Algar35 2019-W26 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar35 2019-W27 7 1 0 0 0 1 0 0 0 0 1 0 1
Algar35 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W30 7 0 0 0 0 2 0 0 0 0 0 0 0
Algar35 2019-W31 7 1 0 0 0 1 0 0 0 0 0 0 0
Algar35 2019-W32 7 0 0 0 0 0 0 0 0 0 0 3 1
Algar35 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W36 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar35 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W39 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar35 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W42 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar35 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 2019-W46 6 0 0 0 0 0 0 0 0 0 0 0 0
Algar37 2018-W45 1 0 0 0 0 0 0 0 0 0 0 0 0
Algar37 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar37 2018-W47 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar37 2018-W48 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar37 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar37 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
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Algar64 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar64 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar64 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar64 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar64 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar64 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar64 2019-W46 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W45 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W46 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W47 7 3 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W48 7 4 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W49 7 3 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W51 7 2 0 0 0 0 0 0 0 0 0 0 0
Algar65 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W00 5 1 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W03 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W04 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W05 7 2 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W06 7 2 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W07 7 2 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W10 7 0 0 0 0 0 0 0 1 0 0 0 0
Algar65 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W14 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W16 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W18 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 2019-W25 6 0 0 0 0 1 0 0 0 0 0 0 0
Algar66 2018-W45 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W18 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar66 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W24 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar66 2019-W25 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar66 2019-W26 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar66 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W30 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W31 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar66 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W36 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar66 2019-W46 3 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W45 3 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W17 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar67 2019-W18 7 0 0 0 0 1 0 0 0 1 0 0 0
Algar67 2019-W19 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W22 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar67 2019-W23 7 0 0 0 0 4 0 0 0 0 0 0 0
Algar67 2019-W24 7 0 0 0 0 2 0 0 0 0 0 0 0
Algar67 2019-W25 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar67 2019-W26 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar67 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W28 7 0 0 0 0 2 0 0 0 1 0 0 0
Algar67 2019-W29 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W30 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W32 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar67 2019-W33 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar67 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W36 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W44 7 0 0 1 0 0 0 0 0 0 0 0 0
Algar67 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 2019-W46 4 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W45 3 1 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W48 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W17 7 0 0 0 0 2 0 0 0 0 0 0 0
Algar68 2019-W18 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W21 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar68 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W25 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W26 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar68 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W30 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W36 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 2019-W46 4 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2018-W45 4 0 0 0 0 0 0 1 0 3 0 0 0
Algar69 2018-W46 7 0 0 0 0 0 0 0 0 3 0 0 0
Algar69 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2018-W48 7 0 0 0 0 0 0 0 0 3 0 0 0
Algar69 2018-W49 7 0 0 0 0 0 0 1 0 1 0 0 0
Algar69 2018-W50 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar69 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W13 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar69 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W15 7 0 0 0 0 0 0 0 0 3 0 0 1
Algar69 2019-W16 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar69 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 2
Algar69 2019-W18 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar69 2019-W19 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W20 7 3 0 0 0 0 0 0 0 0 0 0 1
Algar69 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar69 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar69 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 3
Algar69 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 4
Algar69 2019-W25 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar69 2019-W26 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar69 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar69 2019-W28 7 0 0 0 0 0 0 0 0 3 0 0 1
Algar69 2019-W29 7 0 0 0 0 0 0 0 0 4 0 0 1
Algar69 2019-W30 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar69 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W32 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar69 2019-W33 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar69 2019-W34 7 0 0 0 0 0 0 0 0 3 0 0 0
Algar69 2019-W35 7 0 0 0 0 0 0 0 0 1 0 0 1
Algar69 2019-W36 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W38 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar69 2019-W39 7 1 0 0 0 0 0 0 0 1 0 0 0
Algar69 2019-W40 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar69 2019-W41 7 0 0 0 0 0 0 0 0 3 0 0 0
Algar69 2019-W42 7 0 0 0 0 0 0 1 0 5 0 0 0
Algar69 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 2019-W44 7 0 0 0 0 0 0 1 0 0 0 0 0
Algar69 2019-W45 7 0 0 0 0 0 0 0 0 2 0 0 0
Algar69 2019-W46 3 0 0 0 0 0 0 0 0 1 0 0 0
Algar70 2018-W45 4 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W08 7 0 0 0 0 0 0 0 0 0 1 0 0
Algar70 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W18 7 0 0 0 0 0 0 0 1 0 0 0 0
Algar70 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W20 7 1 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 1
Algar70 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W24 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar70 2019-W25 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W26 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar70 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W30 7 0 0 0 0 0 0 0 0 1 0 0 0
Algar70 2019-W31 7 0 0 0 0 1 0 0 0 0 0 0 0
Algar70 2019-W32 7 0 0 0 0 2 0 0 0 1 0 0 0
Algar70 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W36 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0
Algar70 2019-W46 3 0 0 0 0 0 0 0 0 0 2 0 0

We first need to create a site x occasion matrix for our focal species, Odocoileus virginianus, using a 7 day occasion length. This means we need to break our camera data into seven day bins. We can visualise what this entailes using the camera activity plots in the SingleSiteExploration script:

Where each think black line is an active camera, and each thin grey box represents a potential one-week slice of the detection history.

We can create the detection histories using the following code:

library(dplyr)
library(tidyr)
library(magrittr)
library(tibble)

##Subset to your focal data
tmp <- weekObs[,c("Deployment.Location.ID", "Date", "Odocoileus.virginianus")]

# Turn the counts into presence/absence
tmp$Odocoileus.virginianus[tmp$Odocoileus.virginianus>0] <- 1

# Create a history
detHist <-  tmp %>%
  #Convert from long into wide format (a detection history) using spread
  spread(Date,Odocoileus.virginianus, fill = NA) %>% 
  # group by deployment location
  group_by(Deployment.Location.ID,) %>%
  column_to_rownames( var = "Deployment.Location.ID")

detHist <- as.matrix(detHist)

The resulting data frame looks like this:

2018-W45 2018-W46 2018-W47 2018-W48 2018-W49 2018-W50 2018-W51 2018-W52 2019-W00 2019-W01 2019-W02 2019-W03 2019-W04 2019-W05 2019-W06 2019-W07 2019-W08 2019-W09 2019-W10 2019-W11 2019-W12 2019-W13 2019-W14 2019-W15 2019-W16 2019-W17 2019-W18 2019-W19 2019-W20 2019-W21 2019-W22 2019-W23 2019-W24 2019-W25 2019-W26 2019-W27 2019-W28 2019-W29 2019-W30 2019-W31 2019-W32 2019-W33 2019-W34 2019-W35 2019-W36 2019-W37 2019-W38 2019-W39 2019-W40 2019-W41 2019-W42 2019-W43 2019-W44 2019-W45 2019-W46
Algar08 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar27 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Algar29 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 0
Algar31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar34 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar39 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar61 0 1 1 0 0 1 0 0 0 1 0 0 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1
Algar62 0 0 0 0 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0
Algar63 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1
Algar64 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Algar66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar67 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar68 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Algar69 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1
Algar70 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

It is a matrix of all the weeks the cameras were active, and whether the species was present or absent in that interval. It should mirror the camera activity plot above. The fill = NA command puts a zero where there is data for a given day. However, we have lost our effort information - the number of days each camera was active in a given time period. So we need another data frame!

To get that information we need to create an effort history effHist:

# To create the effort matrix - inst of the Focal Species bring in the effort
effDat <- weekObs[,c("Deployment.Location.ID", "Date", "Effort")]

effHist <-  effDat %>%
  # Create a matrix based on dates and effort
  spread(Date,Effort, fill = NA) %>% 
  # group by deloyment Location ID, then make that the row.namesd
  group_by(Deployment.Location.ID,) %>%
  column_to_rownames( var = "Deployment.Location.ID") 

effHist <- as.matrix(effHist)

Check that it looks sensible:

2018-W45 2018-W46 2018-W47 2018-W48 2018-W49 2018-W50 2018-W51 2018-W52 2019-W00 2019-W01 2019-W02 2019-W03 2019-W04 2019-W05 2019-W06 2019-W07 2019-W08 2019-W09 2019-W10 2019-W11 2019-W12 2019-W13 2019-W14 2019-W15 2019-W16 2019-W17 2019-W18 2019-W19 2019-W20 2019-W21 2019-W22 2019-W23 2019-W24 2019-W25 2019-W26 2019-W27 2019-W28 2019-W29 2019-W30 2019-W31 2019-W32 2019-W33 2019-W34 2019-W35 2019-W36 2019-W37 2019-W38 2019-W39 2019-W40 2019-W41 2019-W42 2019-W43 2019-W44 2019-W45 2019-W46
Algar08 1 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar27 3 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Algar29 3 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 4
Algar31 2 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar32 4 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3
Algar34 1 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar35 3 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6
Algar37 1 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar38 2 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar39 2 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3
Algar61 1 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3
Algar62 1 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar63 2 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar64 1 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5
Algar65 2 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Algar66 2 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3
Algar67 3 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 4
Algar68 3 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 4
Algar69 4 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3
Algar70 4 7 7 7 7 7 7 2 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 3

We might want to remove all of the weeks where we did not get a complete sample:

detHist[effHist!=7] <- NA

Now we are ready to feed this into the unmarked package.

7.1.1 unmarked package

First we must make an unmarked dataframe - a data structure which speaks to the unmakred package architecture:

library(unmarked)
sta <- read.csv("data/raw_data/Example_station_data.csv", header=T)
# Unmarked wants your detection history, effort data and site covariates as matrices.

# Build an unmarkedFramOccu
unDat <- unmarkedFrameOccu(y = detHist,
                          # siteCovs = dataframe with site rows x column variables (in same order as detHist)
                           siteCovs = sta) 
## Warning: siteCovs contains characters. Converting them to factors.

We then fit the model, as before with the proportion of lowland habitat as a predictor:

# Fit general model all variables
m1 <- occu(formula = ~1 # detection formula first
                     ~LOW500, # occupancy formula second,
                data = unDat)

Then view the results.

summary(m1)
## 
## Call:
## occu(formula = ~1 ~ LOW500, data = unDat)
## 
## Occupancy (logit-scale):
##             Estimate  SE     z P(>|z|)
## (Intercept)     6.15 2.9  2.12  0.0339
## LOW500         -7.39 3.6 -2.05  0.0405
## 
## Detection (logit-scale):
##  Estimate    SE     z P(>|z|)
##     -1.63 0.106 -15.4 9.8e-54
## 
## AIC: 617.8133 
## Number of sites: 20
## optim convergence code: 0
## optim iterations: 45 
## Bootstrap iterations: 0

As with the linear models of habitat use, we can generate predictions through the use of a dummy dataframe (newDat), then plot the results:

# Generate new data to predict from 
newDat <- cbind(expand.grid(LOW500=seq(min(sta$LOW500),max(sta$LOW500), length.out=100)))

newDat <- predict(m1, type="state", newdata = newDat, appendData=TRUE)

p1 <- ggplot(newDat, aes(x = LOW500, y = Predicted)) +
  geom_ribbon(aes(ymin = lower, ymax = upper), alpha = 0.5, linetype = "dashed") +
  geom_path(size = 1) +
  labs(x = "Proportion lowland habitat", y = "Occupancy probability") +
  theme_classic() +
  coord_cartesian(ylim = c(0,1))

p1

As with our habitat use model, Odocoileus virginianus occupancy appears to decrease with increasing lowland habitat.

7.3 Further reading

Coming soon!